Automatic quantification of myocardium and pericardial fat from coronary computed tomography angiography: a multicenter study

He, Xiuxiu; Guo, Bang Jun; Lei, Yang; Wang, Tonghe; Tian, Lu; Zhang, Long Jiang; Yang, Xiaofeng

doi:10.1007/s00330-020-07482-5

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…After taking all of this into account, we conclude that the current state-of-the-art fully automatic EAT segmentation methods are not yet robust enough for use in studies or clinical settings, and are not better than manual segmentation. For fully automatic PAT segmentation, He et al [54] evaluate their PAT segmentation method on 422 patients and report Dice coefficients 0.88 or greater, which is slightly lower than reported inter-observer variability.…”

Section: Fully-vs Semi-automatic Resultsmentioning

confidence: 91%

“…Deep learning methods for segmenting PAT are similar to ones used for direct EAT segmentation. For instance, He et al [54] use a 3D-based deep attention U-Net model as in He et al [37], trained on CT images with labeled PAT and myocardium regions. The authors train and evaluate their model on 422 cardiac CT scans from six different centers, to our knowledge the largest cohort used to evaluate EAT and PAT segmentation methods.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

“…While some papers mentioned in this review use comparatively large datasets of 100 patients or more [7,9,25,54], most validate their methods on smaller datasets, even as low as 10 patients. It is our opinion that using a dataset smaller than 100 patients is not sufficient to validate that a method generalizes well enough for medical use, neither on the same distribution of patients as in the dataset nor for patients of other backgrounds.…”

Section: Data Quality Quantity and Diversitymentioning

confidence: 98%

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Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning: A Systematic Review

et al. 2022

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Epicardial and pericardial adipose tissues (EAT and PAT), which are located around the heart, have been linked to coronary atherosclerosis, cardiomyopathy, coronary artery disease, and other cardiovascular diseases. Additionally, the volume and thickness of EAT are good predictors of CVD risk levels. Manual quantification of these tissues is a tedious and error-prone process. This paper presents a comprehensive and critical overview of research on the epicardial and pericardial adipose tissue segmentation and quantification methods, evaluates their effectiveness in terms of segmentation time and accuracy, provides a critical comparison of the methods, and presents ongoing and future challenges in the field. Described methods are classified into pericardial adipose tissue segmentation, direct epicardial adipose tissue segmentation, and epicardial adipose tissue segmentation via pericardium delineation. A comprehensive categorization of the underlying methods is conducted with insights into their evolution from traditional image processing methods to recent deep learning-based methods. The paper also provides an overview of the research on the clinical significance of epicardial and pericardial adipose tissues as well as the terminology and definitions used in the medical literature.

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Section: Fully-vs Semi-automatic Resultsmentioning

confidence: 91%

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Section: Data Quality Quantity and Diversitymentioning

confidence: 98%

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Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning: A Systematic Review

et al. 2022

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“…Studies have done this using numerous algorithmic approaches ( Table 5 ), achieving accuracies up to 98.5%, 36 with excellent correlation with expert readers (Pearson’s correlation, r > 0.924), 37–40 , 42 and almost identical intra-study dice similarity coefficients (DSCs). 36 , 39 , 43 , 44 A similar technique has been used in combination with a fat radiomic profile (FRP) derived from biopsy and CCTA data of perivascular adipose tissue in a retrospective study by Oikonomou et al . 41 to predict MACE at a 5-year follow-up superior to traditional risk stratification tools with an AUC of 0.880 with FRP and an AUC of 0.754 without FRP.…”

Section: Applications Of Machine Learning In Cardiac Computed Tomographymentioning

confidence: 99%

Machine learning applications in cardiac computed tomography: a composite systematic review

Bray

Hanif

Alradhawi

et al. 2022

European Heart Journal Open

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Introduction Artificial intelligence (AI) and machine learning (ML) models are rapidly being applied to analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Methods Six searches were performed in Medline, Embase and the Cochrane Library up to November 2021 for i) Computed Tomography-Fractional Flow Reserve (CT-FFR), ii) atrial fibrillation, iii) aortic stenosis, iv) plaque characterisation, v) fat quantification and vi) coronary artery calcium score. Results We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-fractional flow reserve (FFR) can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification (CAC) and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue (EAT) can also be automatically, accurately and rapidly quantified. Effective ML algorithms have been developed to streamline and optimise the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement (TAVR) valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of atrial fibrillation. Conclusion In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.

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“…En diferentes estudios como los realizados por (Amer, Nassar, Bendahan, Greenspan, & Ben-Eliezer, 2019), (Masoudi et al, 2020), (Dabiri et al, 2020), (He et al, 2021) y (MacLean et al, 2021 utilizaron redes neuronales convoluciones basadas en la arquitectura U-net para la segmentación de grasa en las piernas, abdomen y corazón en CT y MR, obteniendo un rendimiento superior o cercano a 0.9 en cuanto al índice DICE y Jaccard.…”

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Construcción de un prototipo para la segmentación de grasa parafaríngea en imágenes de resonancia magnética

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Automatic quantification of myocardium and pericardial fat from coronary computed tomography angiography: a multicenter study

Cited by 8 publications

References 32 publications

Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning: A Systematic Review

Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning: A Systematic Review

Machine learning applications in cardiac computed tomography: a composite systematic review

Construcción de un prototipo para la segmentación de grasa parafaríngea en imágenes de resonancia magnética

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